The feed forward MLP (FFMLP) architecture used in the previous experiments is effective for testing the impact of GEM on RL and state prediction. Though it is not a SOTA architecture for model based RL. To show that GEM can improve upon SOTA we integrate GEM to PETS [1]}, which is often used as a sophisticated baseline for MBRL. This section experiments stand to confirm that the results from the FFMLP are transferable into more advanced architectures, which can be even stochastic. Figure  shows that GEM+PETS consistently achieves higher rewards on complex environments compared to PETS alone on a similar number of trained samples and networks' parameters.
